{"id":"W1892021512","doi":"10.1002/jwmg.991","title":"Evaluating sources of censoring and truncation in telemetry‐based survival data","year":2015,"lang":"en","type":"article","venue":"Journal of Wildlife Management","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Environment and Protected Areas","funders":"Calgary Institute for the Humanities, University of Calgary; Natural Sciences and Engineering Research Council of Canada; University of Alberta; Alberta Conservation Association; World Wildlife Fund; Humanities Montana; Weyerhaeuser Company","keywords":"Censoring (clinical trials); Statistics; Woodland caribou; Survival analysis; Demography; Econometrics; Biology; Ecology; Mathematics; Predation","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002927638,0.00006660649,0.0001468807,0.000107741,0.00002988751,0.00001286796,0.0002708382,0.00002841094,0.00003656943],"category_scores_gemma":[0.000216622,0.00006014276,0.0000165705,0.0002051879,0.00005606508,0.0003240587,0.0002264575,0.00008713878,0.000004299482],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008395569,"about_ca_system_score_gemma":0.00001488339,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000623048,"about_ca_topic_score_gemma":0.00006355899,"domain_scores_codex":[0.9988,0.0001323768,0.0004166134,0.0001322134,0.0004093143,0.0001094237],"domain_scores_gemma":[0.9992678,0.00007971239,0.0003757208,0.0001988283,0.00002204997,0.00005584502],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006451942,0.00007310117,0.9712661,0.00001757828,0.00001858168,0.000008939231,0.0001661681,0.01171205,0.00002404787,0.00003705576,0.002279017,0.01433284],"study_design_scores_gemma":[0.001227253,0.0001573928,0.9748577,0.00004702368,0.0000449218,0.000004917317,0.0007856713,0.01898603,0.00002487077,0.000260305,0.003532594,0.00007134053],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9956231,0.00004042089,0.000942328,0.002321714,0.0001511257,0.0001053028,0.000001205051,0.00000316056,0.0008116489],"genre_scores_gemma":[0.9881747,0.00002077999,0.01108367,0.0006174123,0.00004648676,0.000001515965,0.000003039366,0.000004992262,0.00004737018],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0142615,"threshold_uncertainty_score":0.2452551,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1081766748653318,"score_gpt":0.330393587913641,"score_spread":0.2222169130483092,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}